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Artificial Intelligence Laboratory LIA


Technologies for search, recommendation and reputation

One of the most important tasks on the WWW is to find items that best satisfy a set of preferences, a problem we call preference-based search. Most sites require the user to specify a fixed set of criteria and then retrieve the most preferred items from a database. However, due to various shortcomings of human decision-making, people are in general not able to state their preferences. Studies show that only a small portion of users actually manage to find their most preferred options, and often end up with suboptimal results. We have developed new mixed-initiative tools using example-critiquing and suggestions, and shown through user studies that they dramatically increase decision accuracy. For more information, see page on preference-based search with suggestions by Paolo Viappiani .

Often, products or other choices cannot be characterized in a vocabulary that would allow people to express their preferences, or people might not even be aware of their preferences. In such cases, they would like to use a recommender system that gives them ideas of items they might like, based on the user's rating of other items. A major problem in existing recommendation systems is the cold-start problem: a relatively large number of ratings is required to provide accurate recommendations. We have developed (and patented) at new technique called ontology filtering that reduces the required information to a practically manageable amount. We are in the process of commercializing this through a startup company, Prediggo.

Another use of product ratings and services is to indicate the reputation for quality. Systems for reputation feedback become increasingly important to help people deal with the anonymity of interactions through the internet. Reputation mechanisms can have two roles: signaling the true quality of products or services, or sanctioning bad quality or dishonest behavior.

We are addressing the design of trustworthy reputation mechanisms that are:

  • incentive-compatible: explicit rewards make it in the best interest of rational agents to report the truth. Honest reporting thus becomes a Nash equilibrium of the system;
  • collusion-resistant: small coalitions of rational agents do not have an interest to coordinate on lying strategies;
  • robust: small noise and errors do not perturb the properties of the mechanism;
  • practical;
Here is an overview of some of our results!

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